Document Organizer MCP Server
The Document Organizer MCP Server provides comprehensive document management capabilities, including PDF-to-Markdown conversion, automated organization, and Universal Project Documentation Standard implementation.
PDF-to-Markdown Conversion: Convert PDFs using
marker(recommended) orpymupdf4llmengines with configurable options for table preservation, image extraction, memory efficiency, and auto-cleaning.Document Discovery & Management: Recursively scan directories to find PDFs, audit conversion status, and efficiently convert only missing Markdown files through automated workflows.
Content Analysis & Organization: Automatically categorize documents (Research, Planning, Technical, Business, etc.) based on content analysis and organize them into structured directory hierarchies.
Full Automation Pipeline: Execute end-to-end workflows that discover, convert, categorize, and organize all documents from start to finish.
Universal Project Documentation Standard: Initialize standardized project structures with essential files like
CURRENT_STATUS.mdandACTIVE_PLAN.md, validate compliance, manage plan lifecycles (ACTIVE, ARCHIVED, SUPERSEDED, BLOCKED), and generate automated progress reports and handoff documentation.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Document Organizer MCP Server convert my research paper PDF to Markdown for the project wiki"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
File Converter MCP
A Model Context Protocol (MCP) server that aggregates various file conversion tools for quick formatting and file type transformations.
Features
Supported Conversions
PDF to Markdown - Convert PDF documents to markdown format
Image Format Conversion - Transform between common image formats (PNG, JPG, WebP, etc.)
Document Conversion - Convert between document formats (DOCX, TXT, HTML, etc.)
Spreadsheet Conversion - Transform spreadsheet formats (CSV, XLSX, JSON, etc.)
Code Format Conversion - Convert between code formats and syntax highlighting
Archive Operations - Extract and create archive files (ZIP, TAR, etc.)
Conversion Engines
PDF Engine: marker (recommended) and pymupdf4llm support
Image Engine: Sharp and ImageMagick integration
Document Engine: Pandoc integration for broad format support
Archive Engine: Built-in Node.js compression libraries
Installation
npm install -g file-converter-mcpDependencies
Install conversion engines based on your needs:
# PDF conversion engines
pip install marker-pdf pymupdf4llm
# Image processing (choose one)
npm install sharp
# OR
brew install imagemagick # macOS
apt-get install imagemagick # Ubuntu
# Document conversion
brew install pandoc # macOS
apt-get install pandoc # Ubuntu
# Archive tools (usually pre-installed)
# zip, unzip, tar, gzipUsage
MCP Configuration
Add to your MCP client configuration:
{
"mcpServers": {
"file-converter": {
"command": "file-converter-mcp",
"args": []
}
}
}Available Tools
PDF Conversion
convert_pdf_to_markdown- Convert PDF files to Markdownextract_pdf_text- Extract plain text from PDF filesextract_pdf_images- Extract images from PDF files
Image Conversion
convert_image_format- Convert between image formatsresize_image- Resize images with quality optionscompress_image- Reduce image file size
Document Conversion
convert_document- Convert between document formats using Pandocextract_document_text- Extract text from various document formatsconvert_markdown_to_html- Convert Markdown to HTML with styling
Spreadsheet Conversion
convert_csv_to_json- Convert CSV data to JSON formatconvert_json_to_csv- Convert JSON data to CSV formatconvert_xlsx_to_csv- Extract CSV data from Excel files
Archive Operations
create_archive- Create ZIP or TAR archives from files/foldersextract_archive- Extract contents from archive fileslist_archive_contents- List files in archive without extracting
Utility Tools
detect_file_type- Identify file format and encodingvalidate_conversion- Check if conversion is supportedbatch_convert- Convert multiple files in one operation
Examples
Basic PDF Conversion
// Convert PDF to Markdown
await client.callTool("convert_pdf_to_markdown", {
input_path: "/path/to/document.pdf",
output_path: "/path/to/output.md",
options: {
engine: "marker",
preserve_formatting: true
}
});Image Format Conversion
// Convert PNG to WebP with compression
await client.callTool("convert_image_format", {
input_path: "/path/to/image.png",
output_path: "/path/to/image.webp",
options: {
quality: 80,
format: "webp"
}
});Document Conversion
// Convert DOCX to Markdown using Pandoc
await client.callTool("convert_document", {
input_path: "/path/to/document.docx",
output_path: "/path/to/document.md",
options: {
format: "markdown",
preserve_styles: false
}
});Batch Operations
// Convert multiple files at once
await client.callTool("batch_convert", {
input_directory: "/path/to/input/",
output_directory: "/path/to/output/",
conversions: [
{ from: "pdf", to: "markdown" },
{ from: "png", to: "webp" },
{ from: "docx", to: "txt" }
]
});Configuration Options
Conversion Settings
interface ConversionOptions {
engine?: string; // Conversion engine to use
quality?: number; // Output quality (1-100)
preserve_formatting?: boolean; // Maintain original formatting
output_format?: string; // Specific output format
compression_level?: number; // Compression level (0-9)
custom_options?: Record<string, any>; // Engine-specific options
}Supported File Types
Input Formats
Documents: PDF, DOCX, DOC, RTF, TXT, HTML, XML
Images: PNG, JPG, JPEG, WebP, GIF, BMP, TIFF, SVG
Spreadsheets: CSV, XLSX, XLS, JSON, TSV
Archives: ZIP, TAR, GZ, 7Z, RAR (extract only)
Code: Various programming language files
Output Formats
Text: Markdown, HTML, TXT, RTF
Images: PNG, JPG, WebP, GIF, BMP
Data: JSON, CSV, XML, YAML
Archives: ZIP, TAR, GZ
Performance Considerations
Memory Usage: Large files are processed in chunks to prevent memory issues
Processing Speed: Different engines have different speed/quality tradeoffs
Batch Processing: More efficient for multiple file conversions
Caching: Converted files can be cached to avoid re-processing
Error Handling
The server provides comprehensive error handling:
Input file validation and format detection
Graceful fallback between conversion engines
Detailed error messages with suggested solutions
Progress tracking for long-running conversions
Development
# Clone repository
git clone https://github.com/cordlesssteve/file-converter-mcp.git
cd file-converter-mcp
# Install dependencies
npm install
# Build project
npm run build
# Run development mode
npm run dev
# Run tests
npm testContributing
Fork the repository
Create a feature branch
Add support for new file formats or conversion engines
Add tests for new functionality
Submit a pull request
License
MIT License - see LICENSE file for details.
Support
Available Tools
8 toolsanalyze_contentC
Analyze markdown files to automatically determine document categories
Inputs: directory_path (required).
check_conversionsC
Analyze PDF collection to determine conversion status
Inputs: directory_path (required).
convert_missingB
Convert only PDFs that lack companion Markdown files
Inputs: directory_path (required).
convert_pdfB
Convert PDF files to Markdown format using marker (recommended) or pymupdf4llm
Inputs: pdf_path (required), output_path.
discover_pdfsB
Recursively scan directory trees to locate all PDF files
Inputs: directory_path (required), recursive.
full_workflowC
Execute end-to-end document organization pipeline
Inputs: directory_path (required).
init_project_docsC
Initialize Universal Project Documentation Standard structure
Inputs: directory_path (required), project_name (required), project_type.
organize_structureC
Create hierarchical directory structure and move files to appropriate locations
Inputs: categories (required), directory_path (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 8 tools
Most tools have distinct purposes, but there is some overlap between 'full_workflow' and the combination of other tools like 'discover_pdfs', 'convert_pdf', and 'organize_structure', which could cause confusion about when to use the integrated workflow versus individual steps. The other tools are clearly scoped to specific tasks like analysis, conversion, discovery, and initialization.
Tool names mostly follow a consistent verb_noun pattern (e.g., analyze_content, check_conversions, convert_pdf), with 'full_workflow' as a minor deviation that uses an adjective instead of a verb. All names are in snake_case and are descriptive, making them readable and predictable, though not perfectly uniform.
With 8 tools, the count is well-scoped for a document organization server, covering key operations from discovery and conversion to analysis and structuring. Each tool appears to serve a specific function without redundancy, fitting a typical range for such a domain.
The tool set covers core document organization workflows, including discovery, conversion, analysis, and structuring, with a full pipeline tool for integration. A minor gap exists in update or delete operations for documents or categories, but agents can likely work around this given the focus on initial organization and conversion.
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